An empirical approach to predict regional organic carbon in deep soils
文献类型：期刊论文
| 作者 | Wang, Jingjing; Wei, Xiaorong1,6; Jia, Xiaoxu5; Huang, Mingbin1,6; Liu, Zhipeng4; Yao, Yufei3; Shao, Mingan1,6 |
| 刊名 | SCIENCE CHINA-EARTH SCIENCES
 |
| 出版日期 | 2023-03-01 |
| 卷号 | 66期号:3页码:583-593 |
| 关键词 | Deep SOC Empirical approach Negative exponential function Depth distribution Spatial pattern |
| DOI | 10.1007/s11430-022-1032-2 |
| 文献子类 | Article |
| 英文摘要 | Deep soil organic carbon (SOC) plays an important role in carbon cycling. Precisely predicting deep SOC at the regional scale is crucial for the accurate assessment of carbon sequestration potential in soils but has been challenging for a century. Herein, we developed a depth distribution function-based empirical approach to predict SOC in deep soils at the regional scale. We validated this approach with a dataset from four regions of the world and examined the application of this approach in China's Loess Plateau. We found that among the reported depth distribution functions describing vertical patterns of SOC, the negative exponential function performed best in fitting SOC along the soil profile in various regions. Moreover, the parameters (i.e., C-e and k) of the negative exponential function were linearly correlated to surface SOC (0-20 cm) and the changing rates of SOC within the topsoil (0-40 cm). Based on the above relationships, the empirical equations for predicting the negative exponential parameters are established. The validation results from site-specific and regional dataset showed that combining the negative exponential function and such empirical equations can precisely predict SOC concentration in soils down to 500 cm depth. Our study provides a simple, rapid and accurate method for predicting deep soil SOC at the regional scale, which could simplify the assessment of deep soil SOC in various regions. |
| WOS关键词 | VERTICAL-DISTRIBUTION ; TURNOVER TIMES ; CLIMATE ; STORAGE ; LITTER ; SEQUESTRATION ; STABILIZATION ; SIMULATION ; PROFILES ; NITROGEN |
| WOS研究方向 | Geology |
| WOS记录号 | WOS:000934814000001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200749]  |
| 专题 | 黄河三角洲现代农业工程实验室_外文论文 |
| 作者单位 | 1.Northwest A&F Univ, State Key Lab Soil Eros & Dryland Farming Loess Pl, Yangling 712100, Peoples R China 2.Northwest Univ, Coll Urban & Environm Sci, Xian 710127, Peoples R China 3.Nanjing Agr Univ, Coll Resources & Environm Sci, Nanjing 210095, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 5.CAS Ctr Excellence Quaternary Sci & Global Change, Xian 710061, Peoples R China 6.Chinese Acad Sci, Inst Soil & Water Conservat, Minist Water Resources, Yangling 712100, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Jingjing,Wei, Xiaorong,Jia, Xiaoxu,et al. An empirical approach to predict regional organic carbon in deep soils[J]. SCIENCE CHINA-EARTH SCIENCES,2023,66(3):583-593. |
| APA | Wang, Jingjing.,Wei, Xiaorong.,Jia, Xiaoxu.,Huang, Mingbin.,Liu, Zhipeng.,...&Shao, Mingan.(2023).An empirical approach to predict regional organic carbon in deep soils.SCIENCE CHINA-EARTH SCIENCES,66(3),583-593. |
| MLA | Wang, Jingjing,et al."An empirical approach to predict regional organic carbon in deep soils".SCIENCE CHINA-EARTH SCIENCES 66.3(2023):583-593. |
入库方式： OAI收割
来源：地理科学与资源研究所
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